Method and apparatus for image classification and halftone detection

ABSTRACT

A method and apparatus for image classification includes a first embodiment, in which halftone detection is performed based on the size of a boundary set between a class of light pixels and a class of dark pixels, and further based upon image information contained within each single image plane (i.e within one color plane). This embodiment of the invention is based upon the distinctive property of images that halftone areas within the image have larger boundary sets than non-halftone areas within the image. A second, equally preferred embodiment of the invention provides a cross color difference correlation technique that is used to detect halftone pixels.

BACKGROUND OF THE INVENTION

1. Technical Field

The invention relates to image processing. More particularly, theinvention relates to image classification and halftone detection,especially with regard to digitized documents, acquired for example bydigital scanning, and the reproduction of such images on digital colorprinters.

2. Description of the Prior Art

Electronic documents contain a variety of information types in variousformats. A typical page of such document might contain both text (i.e.textual information) and images (i.e. image information). These varioustypes of information are displayed and reproduced in accordance with aparticular formatting scheme, where such formatting scheme provides aparticular appearance and resolution as is appropriate for suchinformation and printing device. For example, text may be reproducedfrom a resident font set and images may be reproduced as continuous tone(contone) or halftone representations. In cases where a halftone isused, information about the specific screen and its characteristics(such lines per inch (Ipi)) is also important.

It is desirable to process each type of information in the mostappropriate manner, both in terms of processing efficiency and in termsof reproduction resolution. It is therefore useful to be able toidentify the various information formats within each page of a document.For example, it is desirable to identify halftone portions of a documentand, as appropriate, descreen the halftone information to provide a moreaesthetically pleasing rendition of, e.g an image represented by suchinformation.

In this regard, various schemes are known for performing halftonedetection. See, for example, T. Hironori, False Halftone PictureProcessing Device, Japanese Publication No. JP 60076857 (1 May 1985); I.Yoshinori, I. Hiroyuki, K. Mitsuru, H. Masayoshi, H. Toshio, U. Yoshiko,Picture Processor, Japanese Publication No. JP 2295358 (Dec. 6, 1990);M. Hiroshi, Method and Device For Examining Mask, Japanese PublicationNo. JP 8137092 (May 31, 1996); T. Mitsugi, Image Processor, JapanesePublication No. JP 5153393 (Jun. 18, 1993); J.-N. Shiau, B. Farrell,Improved Automatic Image Segmentation, European Patent Application No.521662 (Jan. 7, 1993); H. Ibaraki, M. Kobayashi, H. Ochi, HalftonePicture Processing Apparatus, European Patent No. 187724 (Sep. 30,1992); Y. Sakano, Image Area Discriminating Device, European PatentApplication NO. 291000 (Nov. 17, 1988); J.-N. Shiau, Automatic ImageSegmentation For Color Documents, European Patent Application No. 621725(Oct. 26, 1994); D. Robinson, Apparatus and Method For Segmenting AnInput Image In One of A Plurality of Modes, U.S. Pat. No. 5,339,172(Aug. 16, 1994); T. Fujisawa, T. Satoh, Digital Image ProcessingApparatus For Processing A Variety of Types of Input Image Data, U.S.Pat. No. 5,410,619 (Apr. 25, 1995); R. Kowalski, D. Bloomberg, HighSpeed Halftone Detection Technique, U.S. Pat. No. 5,193,122 (Mar. 9,1993); K. Yamada, Image Processing Apparatus For Estimating HalftoneImages From Bilevel and Pseudo Halftone Images, U.S. Pat. No. 5,271,095(Dec. 14, 1993); S. Fox, F. Yeskel, Universal Thresholder/Discriminator,U.S. Pat. No. 4,554,593 (Nov. 19, 1985); H. Ibaraki, M. Kobayashi, H.Ochi, Halftone Picture Processing Apparatus, U.S. Pat. No. 4,722,008(Jan. 26, 1988); J. Stoffel, Automatic Multimode Continuous HalftoneLine Copy Reproduction, U.S. Pat. No. 4,194,221 (Mar. 18, 1980); T.Semasa, Image Processing Apparatus and Method For Multi-Level ImageSignal, U.S. Pat. No. 5,361,142 (Nov. 1, 1994); J.-N. Shiau, AutomaticImage Segmentation For Color Documents, U.S. Pat. No. 5,341,226 (Aug.23, 1994); R. Hsieh, Halftone Detection and Delineation, U.S. Pat. No.4,403,257 (Sep. 6, 1983); J.-N. Shiau, B. Farrell,Automatic ImageSegmentation Using Local Area Maximum and Minimum Image Signals, U.S.Pat. No. 5,293,430 (Mar. 8, 1994); and T. Semasa, Image ProcessingApparatus and Method For Multi-Level Image Signal, U. S. U.S. Pat. No.5,291,309 (Mar. 1, 1994).

While there is a substantial volume of art that addresses various issuesassociated with halftone generation and detection, there has notheretofore been available a fast and efficient technique for effectiveimage classification and for detection of halftone segments and othercomponents of a document. In particular, such techniques as are known donot effectively detect halftone information and classify image regions,especially with regard to efficient algorithms based upon such factorsas boundary sets for image information within a single image plane andcross color differences for image information across multiple imagesplanes.

It would be advantageous to provide an improved technique for imageclassification and halftone detection.

It would also be advantageous to provide a technique that has the todetect halftone components of a document without having predeterminedinformation about the halftone technique used to produce the originalimage, and moreover, without having detailed information on the specificcharacteristics of that halftone technique, such as the type of screenused, the threshold array, or the Ipi.

SUMMARY OF THE INVENTION

The invention provides a method and apparatus for image classificationand halftone detection.

In a first embodiment of the invention, image classification andhalftone detection is performed based on the size of a boundary set, andfurther based upon image information contained within a single imageplane (i.e within one color plane). This embodiment of the invention isbased upon the distinctive property of images that halftone areas withinthe image have a larger boundary set than non-halftone areas within theimage.

For example, consider a window of size K×K. In this example, a thresholdT1 is adaptively determined and all pixels having a value <T1 aredeclared to be dark, while all other pixels are declared to be light.This threshold may be set in any of several ways that may include, forexample a histogram technique: a histogram of values may be computed inthe current window. A right peak area and left peak area are then foundin the histogram. If these two areas merge, the threshold is set to themedian, otherwise the threshold is set to the end of the larger peak.

As an alternative to the adaptive threshold, another technique, based ona weighted support decision mechanism, can be used to mark the pixels asdark or light.

Given another threshold T2 and a window in the image, the number ofvertical class changes and horizontal class changes which occur in thewindow is counted, where “class change” means a change from a dark pixelto a light pixel or from a light pixel to a dark pixel. The percentageof light pixels in the window is denoted as p, while the percentage ofdark pixels is denoted as q. The expected number of vertical andhorizontal changes on a K×K window is 4 p q K (K−1).

The type of a current pixel is determined by comparing the actual numberof class changes to the probability based estimate. If the ratio ofthese two numbers is higher than the threshold T2, then the pixel isdeclared a halftone pixel.

In a second, equally preferred embodiment of the invention, cross colordifference correlation is used to detect halftone pixels. This is incontrast to most prior art techniques in which halftone detection andimage region classification methods are applied separately to each colorcomponent.

In this embodiment, for each pixel having R, G, and B components in animage, there is a surrounding K×K window (K odd). The RGB values of thepixels in this window are denoted R(i), G(i), and B(i), where i=0, . . ., k*k−1; and the RGB averages are denoted aR, aG, and aB. It has beenempirically determined that a window size of K=3 or K=5 provides thebest results in terms of cost/performance.

The Euclidean norms of the R( ), G( ), B( ) vectors are denoted IRI,IGI, IBI, and the following sums are computed:

xRG=Σ((R(i)−R)(G(i)−G)) xaRG=Σ((R(i)−aR)(G(i)−aG))

xGB=Σ((G(i)−G)(B(i)−B)) xaGB=Σ((G(i)−aG)(B(i)−aB))

xBR=Σ((B(i)−B)(R(i)−R)) xaBR=Σ((B(i)−aB)(R(i)−aR))

The normalized results xRG/(IRI IGI), . . . correspond to a cosine ofthe angle between components in the window. This angle is relativelysmall for contone and text image information and higher for standardhalftone screens used in color printing, where each color screen istilted differently with respect to the page orientation.

The decision as to whether or not a pixel belongs to the halftone areais made by comparing the results above to a predetermined threshold,which is typically 0.6-0.7.

This detection technique is not as efficient in detecting line screensor screens that are exactly the same for all components and is notapplicable to areas of an image in which a single ink is used.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block schematic diagram of an image processing system whichincludes an image classification and halftone detection module accordingto the invention;

FIG. 2 is a flow diagram of an image reconstruction path which includesan image classification and halftone detection step according to theinvention;

FIG. 3 is a flow diagram illustrating a boundary technique for imageclassification and halftone detection according to the invention;

FIG. 4 is a flow diagram illustrating a cross correlation technique forimage classification and halftone detection according to the invention;and

FIG. 5 is a flow diagram illustrating a combined boundarydetection/cross correlation technique for image classification andhalftone detection according to the invention.

DETAILED DESCRIPTION OF THE INVENTION

The invention provides a method and apparatus for image classificationand halftone detection. The method and apparatus provides twoindependent techniques that may be combined to provide a robust imageclassification and halftone detection scheme. These techniques arereferred to herein as the boundary technique and the cross correlationtechnique, respectively, and are discussed in detail below.

FIG. 1 is a block schematic diagram of an image processing system whichincludes an image classification and halftone detection module accordingto the invention. Image information is provided to the system, either asscanner RGB 15 (e.g. in the case of a digital color copier) or frommemory 10. Also, a scanned image may be cropped by a cropping function12, resulting in a video signal 11. The image information may alsoinclude JPEG data 14.

The image information is decompressed and deblocked, up-sampled, andconverted to RGB as necessary 16. The image information is then providedto an image reconstruction path 21 (discussed in greater detail below inconnection with FIG. 2).

The processed image in RGB or CMYK 22 may be routed to a print engine 24and memory 19. Compression 23 is typically applied to reconstructedimage information that is to be stored in the memory.

FIG. 2 is a flow diagram of an image reconstruction path which includesan image classification and halftone detection step according to theinvention. Scanner RGB 13 is typically input to the image reconstructionpath 21. The data are first subjected to preliminary color adjustment 30and dust and background removal 31. Thereafter, halftone detection 33 isperformed (as is discussed in greater detail below) and the image isdescreened 34. Thereafter, the image is scaled 35, text enhancement isperformed 36, and the image data are color converted 37, producingoutput RGB or CMYK 22 as appropriate for the system print engine.

Boundary Technique

In a first embodiment of the invention, halftone detection is performedbased on the size of a boundary set, and further based upon imageinformation contained within a single image plane (i.e within one colorplane). This embodiment of the invention is based upon the distinctiveproperty of images that halftone areas within the image have a largerboundary set than non-halftone areas within the image. When short onresources, such as computing time or memory, it is of advantage to applythis technique to the intensity component instead of applying itseparately to each of the R,G,B components.

FIG. 3 is a flow diagram illustrating the boundary technique. Theboundary technique may be expressed as follows:

In the neighborhood of every pixel, separate the neighbor pixels intotwo classes, i.e. dark and light (100).

In a neighborhood (which may be different than the neighborhooddescribed above that is used to separate the pixels into classes),measure the size of the boundary between the two classes (110). The sizeof the boundary is estimated according to a probability based model(115). If the ratio between the actual (measured) size and the estimatedsize is less than a threshold T2 which is adaptively computed (120),then the pixel is not a halftone pixel (130); if the ratio between theactual (measured) size and the estimated size is equal to or greaterthan the threshold T2 (120), then the pixel is a halftone pixel (140)and descreening techniques may be applied thereto (150).

For example, consider a window of size K×K. In this example, a thresholdT1 is adaptively determined and all pixels having a value <T1 aredeclared to be dark, while all other pixels are declared to be light.This threshold may be set in any of several ways including, for example,a histogram technique: a histogram of values may be computed in thecurrent window. A right peak area and left peak area are then found inthe histogram. If these two areas merge, the threshold is set to themedian, otherwise the threshold is set to the end of the larger peak.

As an alternative to the adaptive threshold, another technique, based ona weighted support decision mechanism, can be used to mark the pixels asdark or light.

Histogram Technique

A histogram of values may be computed in the current window, thehistogram is analyzed, and a threshold is determined by which the classof a pixel under consideration is set as follows:

If the pixel value is less than the threshold, the pixel is dark; and

If the pixel value is greater than or equal to the threshold, then thepixel is light.

In this technique, a right peak area and left peak area are found in thehistogram. If these two areas intersect, the threshold is set to themedian, otherwise the threshold is set to the end of the larger peak.

Table 1 below is a pseudo code listing showing histogram analysis forthe boundary echnique.

TABLE 1 Histogram Analysis for the Boundary Technique /*  * Histogramanalysis for the “Boundary Method:  * Go from both sides until BRKconsecutive bins have count <=1  * Then compare the group sizes of thedark-class and the light-class,  * and cut closer to the bigger group. *  * The purpose of this piece of code is to compute the  *“class_threshold”:  *  * This number will determine the threshold fromlight to dark  * in the current window. Pixels with higher intensitythan the  * threshold are considered light. The others are dark.  *  *We also compute “class_size” which is the size of the larger class.  *If this size is too big, it is hard to separate the pixels into two  *classes.  *  * We do the following:  *  * If the difference between thelightest pixel to the darkest pixel in  * the window is large enough, weset the threshold to be the median.  * If not, we decide that this isNOT a HT pixel.  *  */ #define BRK 3 #define Histogram_size 32 const intQ_factor=256/Histogram_sizes; /*  * light class  */ light_size=0;brk_cnt=0; light_threshold=0; win_max=−1; for (icnt=Histogram_size−1;icnt>=0; 8cnt−−) { cnt=Histogram [cnt] light_size+=cnt; if(light_size *2<size) median=Q_factor *icnt; if ((win_max==−1_&&;lightt_size>=BRK) win_max=Qfactor * (icnt+1) −1; /*increment or restart  */ brk_cnt=(light_size>=BRK)  * (cnt<=1)  *(brk_cnt+1); if (brk_cnt == BRK) { light_threshold=Q_factor *icnt;break; } } /*  *  dark class  */ dark_size=0; brk_cnt=0;dark_threshold=256 win_min=−1; for (icnt=0; icnt<Histogram_size; icnt++){ cnt=histogram {icnt]; dark_size+=cnt; if (dark_size *2<size)median=Q_factor * (icnt+1); if ( (win_min==−1) && dark_size>=BRK)win_min=Q_factor *icnt; /* increment or restart  */brk_cnt=(dark_size>=BRK)  * (cnt<=1)  * (brk_cnt+1); if (brk_cnt == BRK){ dark_threshold=Q_factor * (icnt+1); break; } } /*  *  set threshold */ win_dif=win_max−win_min; if  (light_size >= dark_size) {class_threshold=light_threshold; class_size=light_size; } else {class_threshold=dark_threshold class_size=dark_size; } if (class_size >= size−BRK) { /* uni-modal population  * if support iswide enough, cut at the median,  * otherwise - do not cut  */ if(win_dif >= BRK *Q_factor) { class_threshold=median; } else {decision+NOT_HT;/* virtually constant block  */ return ( ); } }

Weighted Support Technique.

The following definitions are used in connection with discussion hereinof the weighted support technique:

W=win-width, which is the width of the window to one side. For example,if the window is a 5×5 window, then there are two pixels to each side ofthe central (examined) pixel and the window width is W=2.

WL=win-length, which is the length of the window and which is equal to(2*W)+1.

WS=win-size, which is the window size and which is equal to WL*WL.

N_compares=2*WL*(WL−1)

VB=Vertical boundary, which is the number of pixels that are of adifferent class than the pixel directly above them.

HB=Horizontal boundary, which is the number of pixels that are of adifferent class than the pixel directly to the left of them.

BT=Boundary threshold, which is a parameter set by the application.

LC=Light class, which is the number of light pixels.

DC=Dark class, which is the number of dark pixels.

Algorithm.

Let:

center=intensity of the center pixel;

cnt_d=number of pixels within the window that are darker than the centerpixel;

cnt_l=number of pixels within the window that are lighter than thecenter pixel;

avg_d=average of intensities that are darker than the center pixel;

avg_l=average of intensities that are lighter than the center pixel:

avg=average of intensities in the window; and

dev=standard deviation of intensities in the window.

Then:

threshold=(avg_d+avg_l)/2;

and

D1=(center-avg_d)/(avg_l-center)≦½

D2=center<threshold−8

D3=cnt_d/cnt_l≦⅓

D4=cnt_d/cnt_l≦½

D5=center<50

D6=cnt_d<cnt_l

L1=(avg_l-center)/(center-avg_d)≧½

L2=center>threshold+8

L3=cnt_l/cnt_d≦⅓

L4=cnt_l/cnt_d≦½

L5=center>200

L6=cnt_l<cnt_d

Then:

D_support=5*D1+4*D2+3*D3+2(D4+D5)+D6;

and

 L_support=5*L1+4*L2+3*L3+2(L4+L5)+L6.

If (D_support<L_support), then the center pixel is light;

If (D_support=L_support), and (cnt_L<cnt_D), then the center pixel islight;

Otherwise, the center pixel is dark.

In the event that one of the classes is too small (LC<W or DC<W) (FIG.3: 300), the pixel is not halftone (310). Accordingly, the area examinedis not descreened to avoid loss of shadow details.

Comparison With The Estimation Model

After all of the pixels have been marked with light/dark attributesusing either the histogram technique or the weighted support technique,a final decision is made on the type of pixel (halftone or not halftone)based on the size of the boundary set between light pixels and darkpixels.

To measure the size of the boundary set in a window measuring WL×WL,perform 2*WL*(WL−1) XOR's (Exclusive OR's), where WL(WL−1) XOR's areapplied for the vertical boundary and WL(WL−1) XOR's are for thehorizontal boundary.

Denote N_compares=2*WL*(WL−1). A probabilistic model is then introducedfor a two class population distribution. Assuming for the sake ofsimplicity a binomial model, then the expected number of class changesis equal to N_compares*(pq+qp),

where:

p=probability (dark pixel),

and

q=1−p=probability (light pixel).

Approximate p by DC/WS, q by LC/WS, then the expected size of theboundary, which is denoted by Boundary_expected, is N_compares*2pq.

In accordance with the discussion above, it follows that:

Boundary_expected=(2*N_compares) (LC/WS) (DC/WS)

An external parameter BT allows a degree of freedom when fitting to thebinomial model.

BT is a number between 0 and 1 where a value closer to 1 corresponds toa good binomial approximation.

If the Boundary size=VB+HB<Boundary_expected*BT, mark the pixel as nothalftone;

Else, mark the pixel as halftone.

EXAMPLE—BOUNDARY TECHNIQUE

Class map:

x=dark, o=light.

◯ X ◯ ◯ X ◯ X ◯ ◯ X X X X X X ◯ ◯ ◯ X ◯ ◯ ◯ ◯ ◯ ◯

W=2;

WL=5;

WS=25,

DC=10,

LC=15,

N_Compares=40

VB=8,

HB=8,

BT=0.95.

Boundary_expected=2*40*15/25*10/25=19.2

The value 19.2*0.95=18.24 is not less than 16. Therefore, the pixel isnot a halftone pixel.

Cross-Correlation.

In a second, equally preferred embodiment of the invention, cross colordifference correlation is used to detect halftone pixels. This is incontrast to most prior art techniques in which halftone detection andimage region classification methods are applied separately to each colorcomponent.

In this embodiment, for each pixel having R, G, and B components in animage, there is a surrounding K×K window (K odd). The RGB values of thepixels in this window are denoted R(i), G(i), and B(i), where i=0, . . ., k*k−1; and the RGB averages are denoted aR, aG, and aB. It has beenempirically determined that of window size of K=3 or K=5 provides thebest results in terms of cost/performance.

The Euclidean norms of the R( ), G( ), B( ) vectors are denoted IRI,IGI, IBI, and the following sums are computed:

xRG=Σ((R(i)−R)(G(i)−G)) xaRG=Σ((R(i)−aR)(G(i)−aG))

xGB=Σ((G(i)−G)(B(i)−B)) xaGB=Σ((G(i)−aG)(B(i)−aB))

xBR=Σ((B(i)−B)(R(i)−R)) xaBR=Σ((B(i)−aB)(R(i)−aR))

The normalized results xRG/(IRI IGI), . . . correspond to the cosine ofthe angle between components in the window. This angle is relativelysmall for contone and text image information and higher for standardhalftone screens used in color printing, where each color screen istilted differently with respect to the page orientation.

The decision as to whether or not a pixel belongs to the halftone areais made by comparing the results above to a predetermined threshold,which is typically 0.6-0.7. This detection technique is not as efficientin detecting line screens or screens that are exactly the same for allcomponents and is not applicable to areas of an image in which a singleink is used.

This embodiment of the invention computes the correlation between the Rplane, the G plane, and the B plane inside a square neighborhood of acurrent pixel. A low correlation factor indicates a halftone (HT) areahaving halftone screens that are not the same for all colors. Thismethod is usually used in conjunction with the boundary set method(discussed above) and is a complementary halftone detection method.

The computation is controlled by the following parameters:

W=Window size=The size of a neighborhood window.

MN=Minimal norm=Threshold for minimal norm within a single componentwindow.

T=Correlation threshold=threshold for classifying a single pixel.

A=W×W=area of a window.

FIG. 4 is a flow diagram illustrating a cross correlation technique forimage classification and halftone detection according to the invention.Consider a window of size W×W, with R,G,B components Rij, Gij, Bij andcenter pixel components R,G,B (400). The following discussion describeshow this embodiment of the invention determines whether or not thecurrent pixel should be marked as a halftone candidate.

Computations:

Calculate the variational norm within each single component window(410): N(R), N(G), N(B). Denote Rij=Rij−R, Gij=Gij−G, Bij=Bij−B,

N(R)=(ΣRij²)½

Calculate correlation factors (420).

ΣCor(R,G)=2 if N(R)≦MN or N(G)≦MN

ΣCor(R,G)=|ΣRij Gij |/(N(R) N(G) otherwise

Similarly, define N (G), N(B), Cor(G,B), Cor(B,R).

Compare correlation factors with a threshold T (430). A pixel is markedas a halftone candidate (450) if at least one of the correlation factorsCor(R,G), Cor (G,B), Cor (B,R) is less than T (440). If not, i.e. if allof them are greater or equal to T, the current pixel is marked asnon-halftone (460).

EXAMPLES—CORRELATION TECHNIQUE Example 1

Let W=3, MN=20.00, T=0.35

Neighborhood:

(R) 110 42 58 201 80 43 7 255 255 (G) 81 140 60 90 191 220 37 204 64 (B)200 189 204 196 200 205 187 197 198

N(R)=292.26, N(G)=287.95, N(B)=18.97.

Cor(R,G)=0.243, Cor(G,B)=2, Cor(B,R)=2.

Because 0.243<T=0.35, the center pixel (80,191,200) is marked as HTcandidate.

Example 2

(R) 147 145 237 147 243 131 231 134 146 (G) 72 81 29 75 15 72 24 85 80(B) 251 255 253 250 250 254 255 255 255

N(R)=249.1, N(G)=154.45, N(B)=11.22.

Cor(R,G)=0.99, Cor (G,B)=2, Cor (B,R)=2.

Because all correlation factors are greater than 0.35, the pixel ismarked non-halftone.

Combined Boundary Detection/Cross Correlation Technique.

As discussed above, the boundary detection technique and crosscorrelation technique may be combined. FIG. 5 is a flow diagramillustrating a combined boundary detection/cross correlation techniquefor image classification and halftone detection according to theinvention. The boundary detection technique is preferably applied first(500), using either the histogram technique (520) or the weightedsupport technique (510). If the pixel is not detected to be a halftonepixel by the boundary detection technique (525), the cross correlationtechnique is then applied (530). If at least one technique detects thepixel as being a halftone pixel, then the pixel is marked as a halftonepixel (550). If neither boudnary detetion technique, nor the crossrelation technique detect the pixel as a halftone pixel (525,535), thenthe pixel is marked as a non-halftone pixel (540). This combinedtechnique is extremely accurate but is computationally expensive.However, this technique does provide two levels of determination withregard to pixel type and thus improves the image quality by reliablyapplying descreening techniques to halftone pixels.

Although the invention is described herein with reference to thepreferred embodiment, one skilled in the art will readily appreciatethat other applications may be substituted for those set forth hereinwithout departing from the spirit and scope of the present invention.Accordingly, the invention should only be limited by the Claims includedbelow.

What is claimed is:
 1. A method for image classification and halftonedetection, comprising: selecting a subject pixel; separating neighborpixels about the subject pixel into light and dark classes in a firstneighborhood; measuring a size of a boundary set between the light anddark classes in a second neighborhood; determining if the boundary sizeis less than a first threshold, wherein the subject pixel is not ahalftone pixel; and determining if the boundary size is equal to orgreater than the first threshold, wherein the subject pixel is ahalftone pixel, and wherein descreening techniques may optionally beapplied to the subject pixel.
 2. The method of claim 1, wherein saidfirst threshold is adaptively determined.
 3. The method of claim 2,further comprising the steps of: counting the number of vertical classchanges and horizontal class changes which occur in said secondneighborhood; denoting the percentage of light pixels in said secondneighborhood as p; denoting the percentage of dark pixels as q;determining the type of said subject pixel by comparing the actualnumber of class changes counted to a probability based estimate todetermine a ratio therebetween; and declaring said subject pixel ahalftone pixel if said ratio is higher than a second threshold; whereinsaid subject pixel is otherwise not a halftone pixel.
 4. The method ofclaim 1, wherein the light/dark separation is performed using any of ahistogram technique and a weighted support technique.
 5. The method ofclaim 4, wherein a second threshold is set using said histogramtechnique, the method further comprising: computing a histogram ofvalues in the first neighborhood; analyzing the histogram; anddetermining the threshold.
 6. The method of claim 5, further comprisingthe steps of: finding a right peak area and left peak area in saidhistogram; setting said first threshold to a median if said right peakarea and said left peak area intersect, otherwise setting said firstthreshold to the end of the larger of said right peak area and said leftpeak area.
 7. The method of claim 4, wherein a second threshold is setusing the weighted support technique, the method further comprising:setting: threshold=(avg_d+avg_l)/2; wherein: center=intensity of thesubject pixel; cnt_d=number of pixels within the second neighborhoodthat are darker than the subject pixel; cnt_l=number of pixels withinthe second neighborhood that are lighter than the subject pixel;avg_d=average of intensities that are darker than the subject pixel;avg_l=average of intensities that are lighter than the subject pixel;avg=average of intensities in the second neighborhood; and dev=standarddeviation of intensities in the second neighborhood.
 8. The method ofclaim 7, further comprising the steps of: setting:D1=(center-avg_d)/(avg_l-center)≦½ D2=center<threshold−8D3=cnt_d/cnt_l≦⅓ D4=cnt_d/cnt_l≦½ D5=center<50 D6=cnt_d<cnt_l andL1=(avg_l-center)/(avg-avg__d)≧½ L2=center>threshold+8 L3=cnt_l/cnt_d≦⅓L4=cnt_l/cnt_d≦½ L5=cint>200 L6=cnt_l<cnt_d  wherein:D_support=5*D1+4*D2+3*D3+2(D4+D5)+D6;  andL_support=5*L1+4*L2+3*L3+2(L4+L5)+L6 and; wherein said center pixel islight if (D_support<L_support); said center pixel is light if(D_support=L_support); and (cnt_L<cnt_D) and otherwise said center pixelis dark.
 9. The method of claim 8, wherein a model is introduced for atwo class population distribution, comprising the steps of: performing2*WL(WL−1) exclusive OR, in a window measuring WL×WL, where WL(WL−1)exclusive ORs are applied for a vertical boundary and WL(WL−1) exclusiveORs are applied for a horizontal boundary; denotingN_compares=2*W(WL−1), where there is an expected number of class changesthat is equal to N_compares*(pq+qp), where: p=probability (dark pixel),and q=1−p=probability (light pixel); and approximating p by DC/WS, q byLC/WS, then the expected size of a boundary which is denoted byBoundary_expected, is N_compares*2pq. wherein: W=the width of saidsecond neighborhood to one side; WL=the length of said secondneighborhood, wherein WL is equal to (2*W)+1; WS=the size of the secondneighborhood, which is equal to WL*WL; N_compares=2*WL*(WL−1); VB=thenumber of pixels that are of a different class than a pixel directlyabove them; HB=the number of pixels that are of a different class than apixel directly to the left of them; BT=Boundary threshold; LC=the numberof light pixels; and DC=the number of dark pixels.
 10. The method ofclaim 9, wherein: Boundary_expected=(2*N_compares) (LC/WS) (DC/WS). 11.The mehtod of claim 10, further comprising the step of: marking saidsubject pixel as not halftone if Boundarysize=VB+HB<Boundary_expected*BT; otherwise marking said subject pixel ashalf; wherein an external parameter BT allows a degree of freedom whenfitting to a binamial model.
 12. The method of claim 10, wherein saidsubject pixel is not halftone if LC<W or DC<W.
 13. The method of claim1, further comprising the step of: applying a cross correlationtechnique to detect halftone pixels.
 14. The method of claim 13,comprising the step of: computing a correlation between an R plane, a Gplane, and a B plane inside a square neighborhood of said subject pixel;wherein a low correlation factor indicates a halftone area havinghalftone screens that are not the same for all colors.
 15. The method ofclaim 14, further comprising the steps of: calculating a variationalnorm within a single component window, wherein Rij=Rij−R, Gij=Gij_G,Bij=Bij_B: N(R)=(ΣRij²)^(½) calculating correlation factors:${Cor}\left( {R,{G = \left\{ \begin{matrix}2 & {{{if}\quad {N(R)}} \leqq {{MN}\quad {or}\quad {N(G)}} \leqq {MN}} \\\left| {{\Sigma R}_{ij}G_{ij}} \middle| {/\left( {{N(R)}{N(G)}} \right)} \right. & {otherwise}\end{matrix} \right.}} \right.$

 similarly defining N(G), N(B), Cor(G,B), Cor(B,R); comparingcorrelation factors with a threshold T; marking said subject pixel as ahalftone candidate if at least one of said correlation factors Cor(R,G),Cor(G,B), Cor (B,R) is less than a threshold; and marking said subjectpixel as not halftone if all of said correlation factors are greater orequal to said threshold.
 16. A method for image classification andhalftone detection, comprising the steps of: selecting a subject pixel;separating neighbor pixels about said subject pixel into light and darkclasses in a first neighborhood; measuring the size of a boundarybetween said light and dark classes in a second neighborhood;determining if said boundary size is less than a first threshold,wherein said subject pixel is not a halftone pixel; determining if saidboundary size is equal to or greater than said first threshold, whereinsaid subject pixel is a halftone pixel, and wherein descreeningtechniques may optionally be applied to said subject pixel; and applyinga cross correlation technique to detect halftone pixels.
 17. Anapparatus for image classification and halftone detection, comprising: afirst neighborhood consisting of: a subject pixel; and neighbor pixelsabout said subject pixel; wherein said neighbor pixels about saidsubject pixel are separated into light and dark classes in said firstneighborhood; a second neighborhood, wherein the size of a boundary setbetween said light and dark classes in said second neighborhood ismeasured; means for determining if said boundary size is less than afirst threshold, wherein said subject pixel is not a halftone pixel;means for determining if said boundary size is equal to or greater thansaid first threshold, wherein said subject pixel is a halftone pixel;and means for optionally applying descreening techniques to said subjectpixel.
 18. The apparatus of claim 17, wherein said first threshold isadaptively determined.
 19. The apparatus of claim 18, furthercomprising: means for counting the number of vertical class changes andhorizontal class changes which occur in said second neighborhood; meansfor denoting the percentage of light pixels in said second neighborhoodas p; means for denoting the percentage of dark pixels as q; means fordetermining said first threshold by comparing the actual number of classchanges counted to a probability based estimate to determine a ratiotherebetween; and means for declaring said subject pixel a halftonepixel if said ratio is higher than said a second threshold; wherein saidsubject pixel is otherwise not a halftone pixel.
 20. The apparatus ofclaim 17, wherein said light/dark separation is performed using any of ahistogram technique and a weighted support technique.
 21. The apparatusof claim 20, wherein said light/dark separation is performed using saidhistogram technique, said apparatus further comprising: means forcomputing a histogram of values in said first neighborhood; means foranalyzing said histogram; means for declaring a pixel as darkdarker thana second threshold and declaring said pixel as light if it is lighterthan said second threshold; and means for determining said secondthreshold.
 22. The apparatus of claim 21, further comprising: means forfinding a right peak area and left peak area in said histogram; andmeans for setting said first threshold to a median if said right peakarea and said left peak area intersect, otherwise setting said firstthreshold to the end of the larger of said right peak area and said leftpeak area.
 23. The apparatus of claim 17, wherein said dark/lightseparation is performed using said weighted support technique, saidapparatus further comprising: means for setting:threshold=(avg_d+avg_l)/2; wherein: center=intensity of said subjectpixel; cnt_d=number of pixels within said second neighborhood that aredarker than said subject pixel; cnt_l=number of pixels within saidsecond neighborhood that are lighter than said subject pixel;avg_d=average of intensities that are darker than said subject pixel;avg_l=average of intensities that are lighter than said subject pixel;avg=average of intensities in said second neighborhood; and dev=standarddeviation of intensities in said second neighborhood.
 24. The apparatusof claim 23, further comprising: means for setting:D1=(center-avg_d)/(avg_l-center)≦½ D2=center<threshold−8D3=cnt_d/cnt_l≦⅓ D4=cnt_d/cnt_l≦½ D5=center<50 D6=cnt_d<cnt_l andL1=(avg_l-center)/(avg-avg_d)≧½ L2=center>threshold+8 L3=cnt_l/cnt_d≦⅓L4=cnt_l/cnt_d≦½ L5=cint>200 L6=cnt_l<cnt_d wherein:D_support=5*D1+4*D2+3*D3+2(D4+D5)+D6; andL_support=5*L1+4*L2+3*L3+2(L4+L5)+L6 and; wherein said center pixel islight if (D_support<L_support); said center pixel is light if(D_support=L_support); and (cnt_L<cnt_D) and otherwise said center pixelis dark.
 25. The apparatus of claim 24, wherein a model is introducedfor a two class population distribution, comprising: means forperforming 2*WL(WL−1) exclusive ORs in a window measuring WL×WL, whereWL(WL−1) exclusive ORs are applied for a vertical boundary and WL(WL−1)exclusive ORs are applied for a horizontal boundary; means for denotingN_compares=2*W*(WL−1) where there is an expected number of class changesthat is equal to N_compares*(pq+qp), where: p=probability (dark pixel),and q=1−p=probability (light pixel); and means for approximating pNDC/WS, qN LC/WS, such that the expected size of a boundary which isdenoted by Boundary_expected, is N_compares*2pq; wherein: W=the width ofsaid second neighborhood to one side; WL=the length of said secondneighborhood, wherein WL is equal to (2*W)+1; WS=the size of the secondneighborhood, which is equal to WL*WL; N_compares=2*WL*(WL−1); VB=thenumber of pixels that are of a different class than a pixel directlyabove them; HB=the number of pixels that are of a different class than apixel directly to the left of them; BT=Boundary threshold; LC=the numberof light pixels; and DC=the number of dark pixels.
 26. The apparatus ofclaim 25, wherein: Boundary_expected=(2 *N_compares) (LC/WS) (DC/WS).27. The apparatus of claim 26, further comprising: means for markingsaid subject pixel as not halftone if Boundarysize=VB+HB<Boundary_expected*BT; otherwise marking said subject pixel ashalf; wherein an external parameter BT allows a degree of freedom whenfitting to be a binomial model.
 28. The apparatus of claim 26, whereinsaid subject pixel is not halftone if LC<W or DC<W.
 29. The apparatus ofclaim 17, further comprising: means for applying a cross correlationtechnique to detect halftone pixels.
 30. The apparatus of claim 29,further comprising: means for computing a correlation between an Rplane, a G plane, and a B plane inside a square neighborhood of saidsubject pixel; wherein a low correlation factor indicates a halftonearea having halftone screens that are not the same for all colors. 31.The apparatus of claim 30, further comprising: means for calculating avariational norm within a single component window, wherein Rij=Rij−R,Gij=Gij−G , Bij=Bij−B: N(R)=(ΣRij²)^(½) means for calculatingcorrelation factors: ${Cor}\left( {R,{G = \left\{ \begin{matrix}2 & {{{if}\quad {N(R)}} \leqq {{MN}\quad {or}\quad {N(G)}} \leqq {MN}} \\\left| {{\Sigma R}_{ij}G_{ij}} \middle| {/\left( {{N(R)}{N(G)}} \right)} \right. & {otherwise}\end{matrix} \right.}} \right.$

means for similarly defining N(G), N(B), Cor(G,B), Cor(B,R); means forcomparing correlation factors with a threshold T; means for marking thesubject pixel as a halftone candidate if at least one of the correlationfactors Cor(R,G), Cor(G,B), Cor(B,R) is less than a threshold; and meansfor marking the subject pixel as not halftone if all of the correlationfactors are greater or equal to the threshold.
 32. An apparatus forimage classification and halftone detection, comprising: means forselecting a subject pixel; means for separating neighbor pixels aboutsaid subject pixel into light and dark classes in a first neighborhood;means for measuring the size of a boundary between said light and darkclasses in a second neighborhood; means for determining if said boundarysize is less than a first threshold, wherein said subject pixel is not ahalftone pixel; means for determining if said boundary size is equal toor greater than said first threshold, wherein said subject pixel is ahalftone pixel, and wherein descreening techniques may optionally beapplied to said subject pixel; and means for applying a crosscorrelation technique to detect halftone pixels if said boundary size isnot equal to or greater than said first threshold; means for calculatinga variational norm within a single component window; means forcalculating correlation factors for said subtext pixel; and means forcomparing said correlation factors with a second threshold, wherein saidsubject pixel is a halftone pixel if at least one of said correlationfactors is less than said second threshold.